/*
 * Hivemall: Hive scalable Machine Learning Library
 *
 * Copyright (C) 2015 Makoto YUI
 * Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package hivemall.io;

import hivemall.mix.MixedModel;
import hivemall.utils.collections.IMapIterator;

import javax.annotation.Nonnull;
import javax.annotation.Nullable;

public interface PredictionModel extends MixedModel {

    ModelUpdateHandler getUpdateHandler();

    void configureMix(ModelUpdateHandler handler, boolean cancelMixRequest);

    long getNumMixed();

    boolean hasCovariance();

    void configureParams(boolean sum_of_squared_gradients, boolean sum_of_squared_delta_x, boolean sum_of_gradients);

    void configureClock();

    boolean hasClock();

    void resetDeltaUpdates(int feature);

    int size();

    boolean contains(@Nonnull Object feature);

    void delete(@Nonnull Object feature);

    @Nullable
    <T extends IWeightValue> T get(@Nonnull Object feature);

    <T extends IWeightValue> void set(@Nonnull Object feature, @Nonnull T value);

    float getWeight(@Nonnull Object feature);

    float getCovariance(@Nonnull Object feature);

    <K, V extends IWeightValue> IMapIterator<K, V> entries();

}